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Universal Analytical Forms for Modeling Image Probabilities
September 2002 (vol. 24 no. 9)
pp. 1200-1214

Abstract—Seeking probability models for images, we employ a spectral approach where the images are decomposed using bandpass filters and probability models are imposed on the filter outputs (also called spectral components). We employ a (two-parameter) family of probability densities, introduced in [11] and called Bessel K forms, for modeling the marginal densities of the spectral components, and demonstrate their fit to the observed histograms for video, infrared, and range images. Motivated by object-based models for image analysis, a relationship between the Bessel parameters and the imaged objects is established. Using \big. L^2\hbox{-}{\rm{metric}}\bigr. on the set of Bessel K forms, we propose a pseudometric on the image space for quantifying image similarities/differences. Some applications, including clutter classification and pruning of hypotheses for target recognition, are presented.

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Index Terms:
Image probabilities, spectral analysis, Bessel K forms, clutter classification, target recognition, Gabor filters.
Anuj Srivastava, Xiuwen Liu, Ulf Grenander, "Universal Analytical Forms for Modeling Image Probabilities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1200-1214, Sept. 2002, doi:10.1109/TPAMI.2002.1033212
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